Component Description

Among a sample of participants of all ages, the antibody testing of stored sera specimens from NHANES 2007–2008 was conducted to determine population levels of pre-pandemic cross reactive antibody to the 2009 pandemic influenza A/H1N1 virus and related influenza A/H1N1 viruses prior to the spread of the novel 2009 H1N1 virus. This data release contains the hemagglutination inhibition assay titers to 2009 H1N1 and two past H1N1 influenza viruses.

Eligible Sample

A sample of participants from NHANES 2007-08, aged 1 year and over, and with stored sera were tested.

The selected virus targets were A/California/7/2009, A/Brisbane/59/2007, and A/New Jersey/8/1976.

The HI assay for each target virus was performed in triplicate. The geometric mean HI titers are reported. An HI titer of ≥40 indicates prior infection with an influenza virus antigenically similar to the tested virus.

Data Processing and Editing

Data was received after all the antibody testing was complete. The data were not edited.

Data Access: All data are publicly available.

Laboratory Quality Assurance and Monitoring

Commercial reagents were used for all testing. All QC procedures recommended by the manufacturers were followed.

1. Given the limited sample size it is suggested that prevalence estimates are only be presented by 1-way classifications.

2. Estimates should not be presented for the age group of 1-5 y due to limited sample size.

3. Overall estimates of prevalence for all ages (1+) can be done. However, all other analyses by age should only be presented for ages 6+.

4. All estimates should be weighted, using the adjusted weight, WTH1N1. All variance estimates should account for the complex design (SDMVPSU and SDMVSTRA). The dataset should be merged with the 2007-2008 demographic file and then create a subset of your sample population using the subpopn statement in the SUDAAN procedure (or similar procedure in another statistical package) itself and not in the SAS data step for proper variance calculation.

5. All estimates should be assessed for design effects, relative standard error, and degrees of freedom. Design effects (DEFF) for a variable can be different for age groups. DEFF can be very different for different variables due to differences in variation by geography, by household intra class correlation, and by demographic heterogeneity. Because DEFFs are highly variable for different variables within each 2-year cycle of the continuous NHANES, it is difficult to set a single minimum sample size for analysis. The general statistical consideration is that an estimated proportion should have a relative standard error of 30% or less.

6. For prevalence estimates near 0% or near 100%, standard methods of calculating confidence limits, such as the Wald method, may produce lower limits less than 0% or upper limits greater than 100%. In these cases, it is often recommended to use alternative methods for calculating 95% confidence limits using transformations (such as the logit or arcsine transformation), using the Wilson method, or calculating exact confidence limits such as the Clopper-Pearson approach. For applications to survey data, see Korn and Graubard.